Explore global development with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’. I also used the ‘scales’ package which I loaded here as well.

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.4     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

## 
## Vedhæfter pakke: 'scales'

## Det følgende objekt er maskeret fra 'package:purrr':
## 
##     discard

## Det følgende objekt er maskeret fra 'package:readr':
## 
##     col_factor

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)

## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

unique(gapminder$year)

##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

head(gapminder)

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Why does it make sense to have a log10 scale on x axis?

A logarithmic axis linearises exponential growth. It more clearly shows the development.

Who is the outlier (the richest country in 1952 - far right on x axis)?

The answer is Kuwait. The country had a GDP per capita of 108382.3529 in 1952. I got the answer by isolating the 1952 information about country and GDP per capita into a new dataframe, gdpPercap_1952 and then ordering this in a decreasing order by GDP pr. capita.

gdpPercap_1952 <- gapminder %>% 
  filter(year == 1952) %>% 
  select(country, gdpPercap)
gdpPercap_1952[order(gdpPercap_1952$gdpPercap,decreasing=TRUE),]

## # A tibble: 142 × 2
##    country        gdpPercap
##    <fct>              <dbl>
##  1 Kuwait           108382.
##  2 Switzerland       14734.
##  3 United States     13990.
##  4 Canada            11367.
##  5 New Zealand       10557.
##  6 Norway            10095.
##  7 Australia         10040.
##  8 United Kingdom     9980.
##  9 Bahrain            9867.
## 10 Denmark            9692.
## # … with 132 more rows

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)

I added another argument in the aesthetic mapping for color and tied it to continent. Then I used the scale function to change the axis labels and units to no longer be scientific notations.

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10(labels = label_number())+
  scale_size_continuous(labels = label_number())

What are the five richest countries in the world in 2007?

The world’s five richest countries in 2007 were Norway, Kuwait, Singapore, United States, and Ireland. I did the same thing as with 1952 and just counted the five first countries (there is probably a better way to do this, though).

gdpPercap_2007 <- gapminder %>% 
  filter(year == 2007) %>% 
  select(country, gdpPercap)
gdpPercap_2007[order(gdpPercap_2007$gdpPercap,decreasing=TRUE),]

## # A tibble: 142 × 2
##    country          gdpPercap
##    <fct>                <dbl>
##  1 Norway              49357.
##  2 Kuwait              47307.
##  3 Singapore           47143.
##  4 United States       42952.
##  5 Ireland             40676.
##  6 Hong Kong, China    39725.
##  7 Switzerland         37506.
##  8 Netherlands         36798.
##  9 Canada              36319.
## 10 Iceland             36181.
## # … with 132 more rows

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

See below.

Can you make the axes’ labels and units more readable? Consider expanding the abbreviated labels as well as the scientific notation in the legend and x axis to whole numbers.

I combined question 5 and 6:

anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
  geom_point() +
  labs(title = "Year: {as.integer(frame_time)}", 
       x = "GDP per capita", 
       y = "Life expectancy (years)", 
       size = "Population", 
       colour = "Continent") +
  scale_x_log10(labels = label_number())+
  scale_size_continuous(labels = label_number())+
  transition_time(year)
anim3

Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

I attempted to answer the following question: What was the mean life expectancy across continents the year I turned 1 (1997) versus the year my father turned 31 (1967)?

I tried out a few different ways of answering and illustrating this. Firstly, I created a new dataframe mean_lifeExp6797 in which I only included information about continent, year and life expectancy. I then grouped by continent and year and summarised the life expectancy column into a new column showing the mean values per year per continent. I then filtered the dataframe to only show the years relevant to answering my question, namely 1967 and 1997.

mean_lifeExp6797 <- gapminder %>% 
  select(continent,year, lifeExp) %>% 
  group_by(continent,year) %>% 
  summarise(mean_life_exp = mean(lifeExp)) %>% 
  filter(year==1967|year==1997)

## `summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.

mean_lifeExp6797

## # A tibble: 10 × 3
## # Groups:   continent [5]
##    continent  year mean_life_exp
##    <fct>     <int>         <dbl>
##  1 Africa     1967          45.3
##  2 Africa     1997          53.6
##  3 Americas   1967          60.4
##  4 Americas   1997          71.2
##  5 Asia       1967          54.7
##  6 Asia       1997          68.0
##  7 Europe     1967          69.7
##  8 Europe     1997          75.5
##  9 Oceania    1967          71.3
## 10 Oceania    1997          78.2

Then I tried plotting the information in two different ways. The most tricky part of this was getting the plots to show years as separate values and not as a scale. I fixed this by making a new dataframe, mean_lifeExp_6797_plotting with a column where the values in the ‘year’ column had been converted into characters.

I then made a barplot with this new dataframe comparing the mean life expectancy of all continents in 1967 versus 1997:

mean_lifeExp6797_plotting <- mean_lifeExp6797 %>% 
  mutate(year_character = as.character(year))

ggplot(mean_lifeExp6797_plotting, aes(x = year_character, y = mean_life_exp, fill = continent)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Mean life expectancy per continent in 1967 versus 1997",
       x = "Year",
       y = "Mean life expectancy (years)",
       fill = "Continent")

Finally, I used faceing to create a collection of small barplots, one per continent, comparing mean life expectancy in 1967 versus 1997. By making both types of barplot, you can compare the development of mean life expectancy both within the resepective continents as well as between continents of the world.

ggplot(mean_lifeExp6797_plotting, aes(x = year_character, y = mean_life_exp, fill = year_character)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title="Mean life expectancy per continent in 1967 versus 1997",
       x = "Continent",
       y = "Mean life expectancy (years)",
       fill = "Year") +
  facet_wrap(~ continent)

HW5 - Make Data Move

Agnes Boel Nielsen

05/10/2020